arXiv subjects

Yue Chen

Publications and source records attributed to Yue Chen.

At least 235 records · Page 13Linked to original sources

arXiv2022

Smart Director: An Event-Driven Directing System for Live Broadcasting

Live video broadcasting normally requires a multitude of skills and expertise with domain knowledge to enable multi-camera productions. As the number of cameras keep increasing, directing a live sports broadcast has now become more complicated and challenging than ever before. The broadcast directors need to be much more concentrated, responsive, and knowledgeable, during the production. To relieve the directors from their intensive efforts, we develop an innovative automated sports broadcast directing system, called Smart Director, which aims at mimicking the typical human-in-the-loop broadcasting process to automatically create near-professional broadcasting programs in real-time by using a set of advanced multi-view video analysis algorithms. Inspired by the so-called "three-event" construction of sports broadcast, we build our system with an event-driven pipeline consisting of three consecutive novel components: 1) the Multi-view Event Localization to detect events by modeling multi-view correlations, 2) the Multi-view Highlight Detection to rank camera views by the visual importance for view selection, 3) the Auto-Broadcasting Scheduler to control the production of broadcasting videos. To our best knowledge, our system is the first end-to-end automated directing system for multi-camera sports broadcasting, completely driven by the semantic understanding of sports events. It is also the first system to solve the novel problem of multi-view joint event detection by cross-view relation modeling. We conduct both objective and subjective evaluations on a real-world multi-camera soccer dataset, which demonstrate the quality of our auto-generated videos is comparable to that of the human-directed. Thanks to its faster response, our system is able to capture more fast-passing and short-duration events which are usually missed by human directors.

cs.CV↗

arXiv2021

A New Look at AI-Driven NOMA-F-RANs: Features Extraction, Cooperative Caching, and Cache-Aided Computing

Non-orthogonal multiple access (NOMA) enabled fog radio access networks (NOMA-F-RANs) have been taken as a promising enabler to release network congestion, reduce delivery latency, and improve fog user equipments' (F-UEs') quality of services (QoS). Nevertheless, the effectiveness of NOMA-F-RANs highly relies on the charted feature information (preference distribution, positions, mobilities, etc.) of F-UEs as well as the effective caching, computing, and resource allocation strategies. In this article, we explore how artificial intelligence (AI) techniques are utilized to solve foregoing tremendous challenges. Specifically, we first elaborate on the NOMA-F-RANs architecture, shedding light on the key modules, namely, cooperative caching and cache-aided mobile edge computing (MEC). Then, the potentially applicable AI-driven techniques in solving the principal issues of NOMA-F-RANs are reviewed. Through case studies, we show the efficacy of AI-enabled methods in terms of F-UEs' latent feature extraction and cooperative caching. Finally, future trends of AI-driven NOMA-F-RANs, including open research issues and challenges, are identified.

eess.SP↗

arXiv2021

Charge Photogeneration in Non-Fullerene Organic Solar Cells: Influence of Excess Energy and Electrostatic Interactions

In organic solar cells, photogenerated singlet excitons form charge transfer (CT) complexes, which subsequently split into free charge carriers. Here, we consider the contributions of excess energy and molecular quadrupole moments to the charge separation process. We investigate charge photogeneration in two separate bulk heterojunction systems consisting of the polymer donor PTB7-Th and two non-fullerene acceptors, ITIC and h-ITIC. CT state dissociation in these donor-acceptor systems is monitored by charge density decay dynamics obtained from transient absorption experiments. We study the electric field dependence of charge carrier generation at different excitation energies by time delayed collection field (TDCF) and sensitive steady-state photocurrent measurements. Upon excitation below the optical gap free charge carrier generation becomes less field dependent with increasing photon energy, which challenges the view of charge photogeneration proceeding through energetically lowest CT states. We determine the average distance between electron-hole pairs at the donor-acceptor interface from empirical fits to the TDCF data. The delocalisation of CT states is larger in PTB7-Th:ITIC, the system with larger molecular quadrupole moment, indicating the sizeable effect of the electrostatic potential at the donor-acceptor interface on the dissociation of CT complexes.

cond-mat.mtrl-sci↗

arXiv2021

Pressure-induced metal-insulator transition in oxygen-deficient LiNbO$_3$-type ferroelectrics

Hydrostatic pressure and oxygen vacancies usually have deleterious effects on ferroelectric materials because both tend to reduce their polarization. In this work we use first-principles calculations to study an important class of ferroelectric materials - LiNbO$_3$-type ferroelectrics (LiNbO$_3$ as the prototype), and find that in oxygen-deficient LiNbO$_{3-δ}$, hydrostatic pressure induces an unexpected metal-insulator transition between 8 and 9 GPa. Our calculations also find that strong polar displacements persist in both metallic and insulating oxygen-deficient LiNbO$_{3-δ}$ and the size of polar displacements is comparable to pristine LiNbO$_3$ under the same pressure. These properties are distinct from widely used perovskite ferroelectric oxide BaTiO$_3$, whose polarization is quickly suppressed by hydrostatic pressure and/or oxygen vacancies. The anomalous pressure-driven metal-insulator transition in oxygen-deficient LiNbO$_{3-δ}$ arises from the change of an oxygen vacancy defect state. Hydrostatic pressure increases the polar displacements of oxygen-deficient LiNbO$_{3-δ}$, which reduces the band width of the defect state and eventually turns it into an in-gap state. In the insulating phase, the in-gap state is further pushed away from the conduction band edge under hydrostatic pressure, which increases the fundamental gap. Our work shows that for LiNbO$_3$-type strong ferroelectrics, oxygen vacancies and hydrostatic pressure combined can lead to new phenomena and potential functions, in contrast to the harmful effects occurring to perovskite ferroelectric oxides such as BaTiO$_3$.

cond-mat.mtrl-sci↗

arXiv2021

A novel class of energy-preserving Runge-Kutta methods for the Korteweg-de Vries equation

In this paper, we present a quadratic auxiliary variable approach to develop a new class of energy-preserving Runge-Kutta methods for the Korteweg-de Vries equation. The quadratic auxiliary variable approach is first proposed to reformulate the original model into an equivalent system, which transforms the energy conservation law of the Korteweg-de Vries equation into two quadratic invariants of the reformulated system. Then the symplectic Runge-Kutta methods are directly employed for the reformulated model to arrive at a new kind of time semi-discrete schemes for the original problem. Under the consistent initial condition, the proposed methods are rigorously proved to maintain the original energy conservation law of the Korteweg-de Vries equation. In addition, the Fourier pseudo-spectral method is used for spatial discretization, resulting in fully discrete energy-preserving schemes. To implement the proposed methods effectively, we present a very efficient iterative technique, which not only greatly saves the calculation cost, but also achieves the purpose of practically preserving structure. Ample numerical results are addressed to confirm the expected order of accuracy, conservative property and efficiency of the proposed algorithms.

math.NA↗

arXiv2021

Dynamic Control of Soft Robotic Arm

In this article, the control problem of one section pneumatically actuated soft robotic arm is investigated in detail. To date, extensive prior work has been done in soft robotics kinematics and dynamics modeling. Proper controller designs can complement the modeling part since they are able to compensate other effects that have not been considered in the modeling, such as the model uncertainties, system parameter identification error, hysteresis, etc. In this paper, we explored different control approaches (kinematic control, PD+feedback linearization, passivity control, adaptive passivity control) and summarized the advantages and disadvantages of each controller. We further investigated the robot control problem in the practical scenarios when the sensor noise exists, actuator velocity measurement is not available, and the hysteresis effect is non-neglectable. Our simulation results indicated that the adaptive passivity control with sigma modification terms, along with a high-gain observer presents a better performance in comparison with other approaches. Although this paper mainly presented the simulation results of various controllers, the work will pave the way for practical implementation of soft robot control.

cs.RO↗

arXiv2021

An Improved Surrogate Method for Solving the Energy Storage Optimal Bidding Problem

Energy storage is expected to play an increasingly important role in mitigating variations that come along with the growing penetration of renewable energy. In this paper, we study the optimal bidding of an energy storage unit in a semi-centralized market. The energy storage unit offers its available storage capacity and maximum charging/discharging rate to the operator; then the operator clears the real-time market by minimizing the total cost. The energy storage unit is paid/charged at locational marginal price (LMP). The problem casts down to a bilevel optimization problem with a mixed-integer lower-level. An improved surrogate-based method with the combined spatial-temporal entropy term is developed to solve this problem. Numerical examples demonstrate the scalability, efficiency, and accuracy of the proposed method.

math.OC↗

arXiv2021

Efficient Force Estimation for Continuum Robot

External contact force is one of the most significant information for the robots to model, control, and safely interact with external objects. For continuum robots, it is possible to estimate the contact force based on the measurements of robot configurations, which addresses the difficulty of implementing the force sensor feedback on the robot body with strict dimension constraints. In this paper, we use local curvatures measured from fiber Bragg grating sensors (FBGS) to estimate the magnitude and location of single or multiple external contact forces. A simplified mechanics model is derived from Cosserat rod theory to compute continuum robot curvatures. Least-square optimization is utilized to estimate the forces by minimizing errors between computed curvatures and measured curvatures. The results show that the proposed method is able to accurately estimate the contact force magnitude (error: 5.25\% -- 12.87\%) and locations (error: 1.02\% -- 2.19\%). The calculation speed of the proposed method is validated in MATLAB. The results indicate that our approach is 29.0 -- 101.6 times faster than the conventional methods. These results indicate that the proposed method is accurate and efficient for contact force estimations.

cs.RO↗

arXiv2021

Storage and Transmission Capacity Requirements of a Remote Solar Power Generation System

Large solar power stations usually locate in remote areas and connect to the main grid via a long transmission line. Energy storage unit is deployed locally with the solar plant to smooth its output. Capacities of the grid-connection transmission line and the energy storage unit have a significant impact on the utilization rate of solar energy, as well as the investment cost. This paper characterizes the feasible set of capacity parameters under a given solar spillage rate and a fixed investment budget. A linear programming based projection algorithm is proposed to obtain such a feasible set, offering valuable references for system planning and policy making.

eess.SY↗

arXiv2021

Flexibility Requirement when Tracking Renewable Power Fluctuation with Peer-to-Peer Energy Sharing

Flexible load at the demand-side has been regarded as an effective measure to cope with volatile distributed renewable generations. To unlock the demand-side flexibility, this paper proposes a peer-to-peer energy sharing mechanism that facilitates energy exchange among users while preserving privacy. We prove the existence and partial uniqueness of the energy sharing market equilibrium and provide a centralized optimization to obtain the equilibrium. The centralized optimization is further linearized by a convex combination approach, turning into a multi-parametric linear program (MP-LP) with renewable output deviations being the parameters. The flexibility requirement of individual users is calculated based on this MP-LP. To be specific, an adaptive vertex generation algorithm is established to construct a piecewise linear estimator of the optimal total cost subject to a given error tolerance. Critical regions and optimal strategies are retrieved from the obtained approximate cost function to evaluate the flexibility requirement. The proposed algorithm does not rely on the exact characterization of optimal basis invariant sets and thus is not influenced by model degeneracy, a common difficulty faced by existing approaches. Case studies validate the theoretical results and show that the proposed method is scalable.

math.OC↗

arXiv2021

Peer-to-Peer Energy Sharing: A New Business Model Towards a Low-Carbon Future

The development of distributed generation technology is endowing consumers the ability to produce energy and transforming them into "prosumers". This transformation shall improve energy efficiency and pave the way to a low-carbon future. However, it also exerts critical challenges on system operations, such as the wasted backups for volatile renewable generation and the difficulty to predict behavior of prosumers with conflicting interests and privacy concerns. An emerging business model to tackle these challenges is peer-to-peer energy sharing, whose concepts, structures, applications, models, and designs are thoroughly reviewed in this paper, with an outlook of future research to better realize its potentials.

cs.DC↗

arXiv2021

PreMevE Update: Forecasting Ultra-relativistic Electrons inside Earth's Outer Radiation Belt

Energetic electrons inside Earth's outer Van Allen belt pose a major radiation threat to space-borne electronics that often play vital roles in our modern society. Ultra-relativistic electrons with energies greater than or equal to two Megaelectron-volt (MeV) are of particular interest due to their high penetrating ability, and thus forecasting these >=2 MeV electron levels has significant meaning to all space sectors. Here we update the latest development of the predictive model for MeV electrons inside the Earth's outer radiation belt. The new version, called PreMevE-2E, focuses on forecasting ultra-relativistic electron flux distributions across the outer radiation belt, with no need of in-situ measurements except for at the geosynchronous (GEO) orbit. Model inputs include precipitating electrons observed in low-Earth-orbits by NOAA satellites, upstream solar wind conditions (speeds and densities) from solar wind monitors, as well as ultra-relativistic electrons measured by one Los Alamos GEO satellite. We evaluated a total of 32 supervised machine learning models that fall into four different classes of linear and neural network architectures, and also successfully tested ensemble forecasting by using groups of top-performing models. All models are individually trained, validated, and tested by in-situ electron data from NASA's Van Allen Probes mission. It is shown that the final ensemble model generally outperforms individual models overs L-shells, and this PreMevE-2E model provides reliable and high-fidelity 25-hr (~1-day) and 50-hr (~2-day) forecasts with high mean performance efficiency values. Our results also suggest this new model is dominated by non-linear components at low L-shells (< ~4) for ultra-relativistic electrons, which is different from the dominance of linear components at all L-shells for 1 MeV electrons as previously discovered.

physics.space-ph↗

arXiv2021

DCT and DST Filtering with Sparse Graph Operators

Graph filtering is a fundamental tool in graph signal processing. Polynomial graph filters (PGFs), defined as polynomials of a fundamental graph operator, can be implemented in the vertex domain, and usually have a lower complexity than frequency domain filter implementations. In this paper, we focus on the design of filters for graphs with graph Fourier transform (GFT) corresponding to a discrete trigonometric transform (DTT), i.e., one of 8 types of discrete cosine transforms (DCT) and 8 discrete sine transforms (DST). In this case, we show that multiple sparse graph operators can be identified, which allows us to propose a generalization of PGF design: multivariate polynomial graph filter (MPGF). First, for the widely used DCT-II (type-2 DCT), we characterize a set of sparse graph operators that share the DCT-II matrix as their common eigenvector matrix. This set contains the well-known connected line graph. These sparse operators can be viewed as graph filters operating in the DCT domain, which allows us to approximate any DCT graph filter by a MPGF, leading to a design with more degrees of freedom than the conventional PGF approach. Then, we extend those results to all of the 16 DTTs as well as their 2D versions, and show how their associated sets of multiple graph operators can be determined. We demonstrate experimentally that ideal low-pass and exponential DCT/DST filters can be approximated with higher accuracy with similar runtime complexity. Finally, we apply our method to transform-type selection in a video codec, AV1, where we demonstrate significant encoding time savings, with a negligible compression loss.

eess.SP↗

arXiv2021

Mobile Reconfigurable Intelligent Surfaces for NOMA Networks: Federated Learning Approaches

A novel framework of reconfigurable intelligent surfaces (RISs)-enhanced indoor wireless networks is proposed, where an RIS mounted on the robot is invoked to enable mobility of the RIS and enhance the service quality for mobile users. Meanwhile, non-orthogonal multiple access (NOMA) techniques are adopted to further increase the spectrum efficiency since RISs are capable to provide NOMA with artificial controlled channel conditions, which can be seen as a beneficial operation condition to obtain NOMA gains. To optimize the sum rate of all users, a deep deterministic policy gradient (DDPG) algorithm is invoked to optimize the deployment and phase shifts of the mobile RIS as well as the power allocation policy. In order to improve the efficiency and effectiveness of agent training for the DDPG agents, a federated learning (FL) concept is adopted to enable multiple agents to simultaneously explore similar environments and exchange experiences. We also proved that with the same random exploring policy, the FL armed deep reinforcement learning (DRL) agents can theoretically obtain a reward gain compare to the independent agents. Our simulation results indicate that the mobile RIS scheme can significantly outperform the fixed RIS paradigm, which provides about three times data rate gain compare to the fixed RIS paradigm. Moreover, the NOMA scheme is capable to achieve a gain of 42% in contrast with the OMA scheme in terms of sum rate. Finally, the multi-cell simulation proved that the FL enhanced DDPG algorithm has a superior convergence rate and optimization performance than the independent training framework.

cs.NI↗

arXiv2021

Integrated 3C in NOMA-enabled Remote-E-Health Systems

A novel framework is proposed to integrate communication, control and computing (3C) into the fifth-generation and beyond (5GB) wireless networks for satisfying the ultra-reliable low-latency connectivity requirements of remote-e-Health systems. Non-orthogonal multiple access (NOMA) enabled 5GB network architecture is envisioned, while the benefits of bringing to the remote-e-Health systems are demonstrated. Firstly, the application of NOMA into e-Health systems is presented. To elaborate further, a unified NOMA framework for e-Health is proposed. As a further advance, NOMA-enabled autonomous robotics (NOMA-ARs) systems and NOMA-enabled edge intelligence (NOMA-EI) towards remote-e-Health are discussed, respectively. Furthermore, a pair of case studies are provided to show the great performance enhancement with the use of NOMA technique in typical application scenarios of 5GB in remote-e-Health systems. Finally, potential research challenges and opportunities are discussed.

eess.SP↗

arXiv2021

Tendon-Driven Soft Robotic Gripper for Berry Harvesting

Global berry production and consumption have significantly increased in recent years, coinciding with increased consumer awareness of the health-promoting benefits of berries. Among them, fresh market blackberries and raspberries are primarily harvested by hand to maintain post-harvest quality. However, fresh market berry harvesting is an arduous, costly endeavor that accounts for up to 50% of the worker hours. Additionally, the inconsistent forces applied during hand-harvesting can result in an 85% loss of marketable berries due to red drupelet reversion (RDR). Herein, we present a novel, tendon-driven soft robotic gripper with active contact force feedback control, which leverages the passive compliance of the gripper for the gentle harvesting of blackberries. The versatile gripper was able to apply a desired force as low as 0.5 N with a mean error of 0.046 N, while also holding payloads that produce forces as high as 18 N. Field test results indicate that the gripper is capable of harvesting berries with minimal berry damage, while maintaining a harvesting reliability of 95% and a harvesting rate of approximately 4.8 seconds per berry.

cs.RO↗

arXiv2021

MIMO Assisted Networks Relying on Intelligent Reflective Surfaces

Intelligent reflective surfaces (IRSs) are invoked for improving both spectral efficiency (SE) and energy efficiency (EE). Specifically, an IRS-aided multiple-input multiple-output network is considered, where the performance of randomly roaming users is analyzed by utilizing stochastic geometry tools. As such, to distinguish the superposed signals at each user, the passive beamforming weight at the IRSs and detection weight vectors at the users are jointly designed. As a benefit, by adopting a zero-forcing-based design, the intra-cell interference imposed by the IRS can be suppressed. In order to evaluate the performance of the proposed network, we first derive the approximated channel statistics in the high signal-to-noise-ratio (SNR) regime. Then, we derive the closed-form expressions both for the outage probability and for the ergodic rate of users. Both the high-SNR slopes of ergodic rate and the diversity orders of outage probability are derived for gleaning further insights. The network's SE and EE are also derived. Our numerical results are provided to confirm that: i) the high-SNR slope of the proposed network is one; ii) the SE and EE can be significantly enhanced by increasing the number of IRS elements.

cs.IT↗

arXiv2021

An Energy Sharing Mechanism Achieving the Same Flexibility as Centralized Dispatch

Deploying distributed renewable energy at the demand side is an important measure to implement a sustainable society. However, the massive small solar and wind generation units are beyond the control of a central operator. To encourage users to participate in energy management and reduce the dependence on dispatchable resources, a peer-to-peer energy sharing scheme is proposed which releases the flexibility at the demand side. Every user makes decision individually considering only local constraints; the microgrid operator announces the sharing prices subjective to the coupling constraints without knowing users' local constraints. This can help protect privacy. We prove that the proposed mechanism can achieve the same disutility and flexibility as centralized dispatch, and develop an effective modified best response based algorithm for reaching the market equilibrium. The concept of absorbable region is presented to measure the operating flexibility under the proposed energy sharing mechanism. A linear programming based polyhedral projection algorithm is developed to compute that region. Case studies validate the theoretical results and show that the proposed method is scalable.

math.OC↗